Future Trajectories Research Articles

Key stakeholder voices: Investigating student perceptions of teachers’ use of assessment for learning

Many schools aim to implement Assessment for Learning (AfL) to stimulate students to take more ownership of their learning and develop self-regulatory skills. This survey-based study is among the few in the field showing how students experience the extent of implementation of AfL, here in English language and mathematics classes in 12 Dutch secondary schools. Analysis showed no differences between the subjects. Students (N = 685) experienced activities to clarify the learning intentions and success criteria and eliciting evidence on their learning progress regularly (between 50–74% of the lessons). Students hardly ever experienced activities aimed at peer- and self-assessment (less than 25% of the lessons). However, cluster analysis revealed three distinct clusters related to the extent of AfL strategy use students experienced. Overall, we can conclude that AfL is not yet fully integrated into teaching practices. Teachers’ skills, knowledge and attitudes required to increase student engagement in AfL practices and strengthen students’ self-regulated learning need more attention in future teacher professional development trajectories.

Atopic March and Disease Modification

Over generations of health care providers, the approach to medicine has predominantly been reactive in nature, addressing symptoms as they arise. With advancements in medical research that explore the underlying mechanisms of disease, associated comorbidities, and improved therapeutic options, the objectives of medicine are transforming. While managing active symptoms remains crucial, there is a growing focus on altering the future trajectory of a disease which will result in new avenues to improve the quality of life of patients. In this evolving landscape, the atopic march emerges as a promising area for disease modification.

El acceso a la justicia arbitral: reflexiones sobre el amparo de pobreza en los procesos arbitrales en Colombia

This article explores the complex legal and constitutional implications of granting fee waivers in arbitration proceedings in Colombia. Fee waivers are a constitutional guarantee that exempts individuals from certain procedural costs in legal proceedings due to economic hardship, ensuring access to justice. However, its application in arbitration has been a subject of intense debate, as this figure is generally specific to ordinary courts. In this context, the inherent costs of arbitration, which are often higher than ordinary court proceedings, are analyzed. The principle of party autonomy is also considered, whereby agreeing to arbitration implies acceptance of associated costs. Critics argue that granting fee waivers could undermine this autonomy and the binding force of the arbitration agreement. Additionally, concerns about potential abuse and the contractual nature of arbitration as distinct from the judicial system are examined. Proponents and some scholars argue that denying fee waivers could effectively prevent those facing extreme economic difficulties from accessing arbitration, violating fundamental rights to access to justice and equality before the law. The article examines proposals for the exceptional and limited application of fee waivers in arbitration, subject to stringent standards. Alternative mechanisms to improve access to arbitral justice for the economically disadvantaged, such as subsidized services, legal aid programs, and specialized arbitration centers, are also discussed. The importance of ensuring equitable and equal access to arbitral justice to maintain the integrity, legitimacy, and effectiveness of arbitration as a dispute resolution mechanism is emphasized. A balanced approach is required that respects the principles of arbitration while upholding both constitutional and conventional guarantees. The article concludes with a call for reflection, open debate, and collaboration within the legal community to address this complex issue and shape the future trajectory of arbitration in Colombia.

A Policy and Evaluation Framework for Sustainable Transitions - An Energy Policy Approach

The climate change and the social crisis launched the basis for the policy discussion about future trajectories of development and sustainability. Innovation policies are expected to promote the transformation of complex socio-technical systems and call for fundamentally different societal production and consumption patterns. This is strictly associated with energy systems and energy policies. A policy framework was conceptualized considering three levels approach: the policy shape at the micro level, the policy context at the meso level and the policy universe at the landscape level. Possible failures were classified according to the levels. To minorize these failures, an evaluation framework was developed considering both the policy outputs and the social impact outcomes - an example of renewable energy communities was applied to the policy and evaluation framework. The intended outcome is a reconfiguration of sociotechnical innovation systems through new technologies’ development, new actor-network and institutional reconfiguration promoting changes in economic dynamics as well as the development of new social competencies with learning, reflexivity and feedback loops for overcoming obstacles.

VOSTN: Variational One-shot Transformer Network for Pedestrian Trajectory Prediction

The accurate and reliable prediction of pedestrian future trajectories is of crucial significance for ensuring the safe navigation of autonomous driving systems. This paper introduces a novel approach called the Variational One-Shot Transformer Network (VOSTN) for the prediction of future trajectories within the 2D on-board domain. VOSTN presents an innovative method called the One-Shot Generation Block, which aims to generate queries simultaneously in order to predict future trajectories in a parallel manner. The utilization of the parallel prediction effectively addresses the issue of error accumulation resulting from autoregression, improves the efficiency of inference time, and enhances the precision of long-term trajectory prediction. And the cross-attention module investigates the inter-relationship between trajectory and ego-motion. Given the inherent stochasticity of pedestrian movement, we employ the Conditional Variational AutoEncoder to forecast available multimodal trajectories. Experimental results demonstrate that our model effectively exploits the information associated with trajectory and ego-motion, leading to the acquisition of more comprehensive feature representations. Moreover, our model outperforms the performance of existing methods on the two 2D on-board domain datasets. Our deterministic/multimodal prediction models show a reduction in the bounding box center final displacement error by 8% / 9% and 0.7% / 3% on PIE and JAAD, respectively, when compared to the most optimal baseline.

Christensenella minuta, a new candidate next-generation probiotic: current evidence and future trajectories.

As the field of probiotic research continues to expand, new beneficial strains are being discovered. The Christensenellaceae family and its newly described member, Christensenella minuta, have been shown to offer great health benefits. We aimed to extensively review the existing literature on these microorganisms to highlight the advantages of their use as probiotics and address some of the most challenging aspects of their commercial production and potential solutions. We applied a simple search algorithm using the key words "Christensenellaceae" and "Christensenella minuta" to find all articles reporting the biotherapeutic effects of these microorganisms. Only articles reporting evidence-based results were reviewed. The review showed that Christensenella minuta has demonstrated numerous beneficial properties and a wider range of uses than previously thought. Moreover, it has been shown to be oxygen-tolerant, which is an immense advantage in the manufacturing and production of Christensenella minuta-based biotherapeutics. The results suggest that Christensenellaceae and Christensenella munita specifically can play a crucial role in maintaining a healthy gut microbiome. Furthermore, Christensenellaceae have been associated with weight management. Preliminary studies suggest that this probiotic strain could have a positive impact on metabolic disorders like diabetes and obesity, as well as inflammatory bowel disease. Christensenellaceae and Christensenella munita specifically offer immense health benefits and could be used in the management and therapy of a wide range of health conditions. In addition to the impressive biotherapeutic effect, Christensenella munita is oxygen-tolerant, which facilitates commercial production and storage.

Estimating the Past and Future Trajectory of LUCC on Wetland Ecosystem Service Values in the Yellow River Delta Region of China

Land use/cover change (LUCC) can impact the provision of ecosystem service values (ESVs), particularly in wetland regions that are subject to frequent and unsustainable land conversions. Exploring the past and future trajectory of LUCC and its effects on ESV has a great significance for wetland management and habitat stability. This study tried to reveal the patterns and magnitude of LUCC on ESV under varying land development scenarios in the Yellow River Delta region, which is a typical region undergoing serious degradation in China. In this study, a combined approach utilizing equivalent coefficients of ecosystem services was employed to determine the ESV of the wetland in relation to the major land use types (LUTs). The Markov–FLUS model was then used to simulate LUTs across multiple scenarios in 2030 and to clarify the relationship of ESV between wetland and other LUTs. The results indicated that the wetland was severely degraded, with a loss in area of 6679.89 ha between 2000 and 2020. Cropland and water body were the main sources of diversion and turnover for the wetland, respectively. Despite the multiple scenario projections revealed, the wetland area exhibited a similar growth rate and a homogeneity in ESV under the natural development (ND), urban construction and development (UCD), and the ecological development (ED) scenarios. The ED scenario was deemed the optimal development strategy for the wetland ecosystem. Our research will improve the comprehension of land development decisions and promote sustainable development in estuarine wetland areas.

Motion Planning for Autonomous Driving with Real Traffic Data Validation

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning, which enables safe autonomous driving on public roads. In this paper, a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method. To begin with, a trajectory prediction model is established based on the graph neural network (GNN) that is trained utilizing the INTERACTION dataset. Then, the validated trajectory prediction model is used to predict the future trajectories of surrounding road users, including pedestrians and vehicles. In addition, a GNN prediction model-enabled motion planner is developed based on the model predictive control technique. Furthermore, two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach, i.e., merging and roundabout scenarios. The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

DNA metabarcoding and morphological identification reveal similar richness, taxonomic composition and body size patterns among flying insect communities

Abstract Tracking insect biodiversity trends and predicting future trajectories is limited by a lack of monitoring data across large spatiotemporal scales. DNA metabarcoding is a time‐ and cost‐efficient method to obtain these data, but has several potential limitations in comparison to traditional morphological approaches. For example, metabarcoding may miss low‐abundance or smaller bodied individuals and is limited to presence/absence information, which may bias estimates of biodiversity and community composition. However, few comparisons of metabarcoding and morphology‐based identification have been made using insects, the by far most diverse animal taxonomic group. Here, we compared terrestrial insect communities identified via morphology versus metabarcoding across four different habitats and three seasons (late spring, mid‐summer and early autumn) during 2019. We compared identified species and community richness, composition and body size, with a focus on key taxonomic groups of bees, true bugs, butterflies and hoverflies. We identified 252 total species, with 54.8% identified by both methods, whereas 21.4% and 19.8% were solely detected with metabarcoding or morphology, respectively. Overall, total community richness, taxonomic composition and community body size were similar between methods. Metabarcoding detection successes declined in low‐abundance and smaller taxa, particularly bees, hoverflies and true bugs; however, species richness of hoverflies and butterflies tended to be higher compared with morphological identification. Our results show that metabarcoding can provide an accurate overview of insect community differences that are comparable to those determined via morphological identification. We recommend that insect monitoring programmes consider incorporating metabarcoding, although future research is needed to overcome some remaining limitations.

Tree ring segmentation using UNEt TRansformer neural network on stained microsections for quantitative wood anatomy.

Forests are critical in the terrestrial carbon cycle, and the knowledge of their response to ongoing climate change will be crucial for determining future carbon fluxes and climate trajectories. In areas with contrasting seasons, trees form discrete annual rings that can be assigned to calendar years, allowing to extract valuable information about how trees respond to the environment. The anatomical structure of wood provides highly-resolved information about the reaction and adaptation of trees to climate. Quantitative wood anatomy helps to retrieve this information by measuring wood at the cellular level using high-resolution images of wood micro-sections. However, whereas large advances have been made in identifying cellular structures, obtaining meaningful cellular information is still hampered by the correct annual tree ring delimitation on the images. This is a time-consuming task that requires experienced operators to manually delimit ring boundaries. Classic methods of automatic segmentation based on pixel values are being replaced by new approaches using neural networks which are capable of distinguishing structures, even when demarcations require a high level of expertise. Although neural networks have been used for tree ring segmentation on macroscopic images of wood, the complexity of cell patterns in stained microsections of broadleaved species requires adaptive models to accurately accomplish this task. We present an automatic tree ring boundary delineation using neural networks on stained cross-sectional microsection images from beech cores. We trained a UNETR, a combined neural network of UNET and the attention mechanisms of Visual Transformers, to automatically segment annual ring boundaries. Its accuracy was evaluated considering discrepancies with manual segmentation and the consequences of disparity for the goals of quantitative wood anatomy analyses. In most cases (91.8%), automatic segmentation matched or improved manual segmentation, and the rate of vessels assignment to annual rings was similar between the two categories, even when manual segmentation was considered better. The application of convolutional neural networks-based models outperforms human operator segmentations when confronting ring boundary delimitation using specific parameters for quantitative wood anatomy analysis. Current advances on segmentation models may reduce the cost of massive and accurate data collection for quantitative wood anatomy.

Computational components of visual predictive coding circuitry.

If a full visual percept can be said to be a 'hypothesis', so too can a neural 'prediction' - although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of 'predictive coding', at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, 'precision' neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a 'prediction'? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects' expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.

Resourcing Their Own Aspirations: First-In-Family Young People and DIY Career Counselling

ABSTRACT The relationship between career counselling and widening participation is increasingly capturing the attention of educational researchers, especially those interested in its social justice implications. International research on first-in-family students demonstrates the continual class-based barriers they are faced with which influence their progression into and through higher education. Career counselling has an important role to play in both supporting first-in-family students to not only enter university but also set them on a career trajectory which allows them to fulfil their aspirations. However, access to effective forms of career counselling remains fragmented and highly dependent on social class. This article explores how first-in-family students engage in what we call ‘DIY career counselling,’ where they draw on their informal networks and the internet to enhance their understanding of their future prospects and possible trajectories. Their efforts suggest a strong desire to learn more about what university can offer them and how they can position themselves advantageously in the job market; furthermore, their efforts also suggest a consumerist awareness that the career counselling on offer in their secondary schools was lacking.

Multi-vehicle trajectory prediction and control at intersections using state and intention information

Traditional deep learning approaches for prediction of future trajectory of multiple road agents rely on knowing information about their past trajectory. In contrast, this work utilizes information of only the current state and intended direction to predict the future trajectory of multiple vehicles at intersections. Incorporating intention information has two distinct advantages: (1) It allows to not just predict the future trajectory but also control the multiple vehicles. (2) By manipulating the intention, the interaction among the vehicles is adapted accordingly to achieve desired behavior. Both these advantages would otherwise not be possible using only past trajectory information Our model utilizes message passing of information between the vehicle nodes for a more holistic overview of the environment, resulting in better trajectory prediction and control of the vehicles. This work also provides a thorough investigation and discussion into the disparity between offline and online metrics for the task of multi-agent control. We particularly show why conducting only offline evaluation would not suffice, thereby necessitating online evaluation. We demonstrate the superiority of utilizing intention information rather than past trajectory in online scenarios. Lastly, we show the capability of our method in adapting to different domains through experiments conducted on two distinct simulation platforms i.e. SUMO and CARLA. The code for this work can be found on the project page here: https://dekai21.github.io/Multi_Agent_Intersection/.

Performance Services for COVID-19 with Private Medical College Hospitals

Corona is a pandemic disease to spread in the human body as a wide-reaching in the history of unwanted world. Yet Medical higher authorities are facing the undesirable spreading causes of this disease as a vital global issue to the present and rationalized generations. Everyone worries of its augmentation around the world and someone suffers from this disease but none can invent effective measures till date as per recovery system. The study aims to assess the management performance services of COVID-19 at North East Medical College and Hospital (NEMCH), as a private medical institution in Sylhet, Bangladesh. Quantitative and qualitative patients' data were obtained from hospital health information centre and secondary data were collected from diverse sources. Key health information instruments of COVID-19 patients and their sustained living status challenges in risks with health rights are highlighted. The research focuses the 41-60 aged group is 42.2%, which is the highest admitted patients and the ratio of male and female is 2:1.13. The study represents the 69.26% of suspected, 30.74% positive and 16.79% death, out of 911 admitted patients from June to August 2020. These findings reflect the health security that the physicians provide. Scientific healthcare knowledge is essential for corona treatment with clinical supports and modern technology but such knowledge is below par. The research suggests future research trajectories of a new alternative treatment options to stimulate the management performance on the priority of National Health Policy and Sustainable Development Goals 2030.

Energy as the new frontier: Dynamic panel data analysis revealing energy's transformative role in economic growth and technological progress

This research presents a novel exploration of the Cobb–Douglas production model by integrating energy as a critical input alongside labor and capital, a relatively underexplored dimension in the context of real GDP analysis. Through a comprehensive study of over 5500 observations across 126 countries, we employed diverse, state-of-the-art econometric techniques to reveal that energy plays a significant and consistently positive role in driving economic growth, alongside the traditional factors of labor and capital. This finding was consistently robust across various model specifications in both static and dynamic frameworks (heterogeneous production function). Incorporating energy as a key variable in the Cobb–Douglas framework marks a substantial contribution to the model's application to contemporary global energy consumption and sustainability challenges. This study not only reinforces the traditional understanding of economic growth drivers but also underscores the pivotal role of energy, offering new insights for policy formulation aimed at sustainable development. Our research enriches the discourse in the field of technological forecasting and social change, providing empirical evidence that highlights the integral role of energy in economic production processes and its broader implications in shaping future economic trajectories.

Study on Gender Ideology Evolution in the "Star Wars" Film Series

With the progression of time, female roles in science fiction movies have become increasingly significant, showcasing notable changes in both their quantity and character traits. Spanning 42 years, the "Star Wars" film series of 11 movies exhibits marked differences in female character portrayal. Over several years of development, female roles have gradually moved away from the singular identity of being "objects of gaze," no longer solely passive "desires of objects" or mere carriers for the flat representation of male characters in the audience's eyes and within the films. In the current narrative mechanism of Star Wars movies, the patriarchal center has been significantly challenged, and the "female discourse subject" with agency and independent will is gradually emerging. This study aims to explore the relevant connections between these female characters and the feminist trends of the time. By decoding the imagery of women in mainstream commercial films under the patriarchal framework, it analyzes the underlying gender ideologies and attempts to explore the future trajectory of constructing female discourse.

Hesitancy among Iranian nursing students regarding future career trajectory: a qualitative analysis

BackgroundThe shortage of skilled nurses is a major concern for health systems worldwide. This may be partly due to the hesitancy of some nurses to enter or remain in the nursing career. This shortage consequently reduces the quality of standard patient care, increases patients’ length of stay in a hospital, increases medical costs, and results in patients’ dissatisfaction. This study aimed to explore hesitancy among senior undergraduate nursing students to pursue a career in nursing.MethodsThis qualitative study adopted a thematic analysis approach. The population comprised senior undergraduate nursing students at Isfahan University of Medical Sciences, Iran, who indicated that they were hesitant to pursue a career in nursing. The study sampling was performed from May 2021 till February 2022 and continued until data saturation. Twenty-four interviews were conducted with the selected students. The attributes related to hesitancy among senior undergraduate nursing students to pursue a career in nursing were extracted as themes and sub-themes.ResultsFour themes were identified: academic idiosyncrasies, individual characteristics, poor nursing market regulations (sub-themes: nursing as a tough and intense career, and unfavorable employment contracts), and the peculiarities of the workplace (sub-themes: conflict within work environment, and barriers to professional nursing practice).ConclusionsThe findings of this study showed that senior undergraduate nursing students weigh their future career options from various academic, personal, professional, and work environment dimensions. The findings provide new insights for decision makers to design and implement innovative strategies to promote retention in nursing careers. We recommend to provide academic counseling for all students and applicants of nursing before they enter the nursing education. Furthermore, we suggest to improve study and work environments, and to implement incentive programs to enhance enthusiasm of nursing students for pursuing a nursing career.

Digitization of Macro-Logistics Systems in Ukraine

This article delves into the multifaceted realm of digitizing macrologistical systems, scrutinizing their nature, influencing factors, and emerging trends. It intricately explores how the integration of digital technologies empowers transport enterprises to deliver superior logistics services while concurrently curtailing costs. A comparative analysis meticulously dissects various facets of the macrologistical system, elucidating the interplay and correlation of its elements within the realms of commodity-money and contractual relationships. The study accentuates the pivotal role of the macro-logistics system in supply chain management.In scrutinizing Ukraine’s Logistics Performance Index scores vis-à-vis EU countries, the authors attribute the disparity to the enduring conflict. A notable European logistics market trend is identified: a surge in digitization investments by individual companies and the establishment of cohesive online platforms. The paper advocates for the integration of automation in transport logistics, particularly in the backdrop of a wartime economy.European standards for a contemporary transport management system are analyzed, culminating in the selection of the CargoClix logistics platform. The article expounds on the platform’s merits in adeptly digitizing the operations of transport and logistics enterprises, offering real-time tracking crucial for prompt responses amid wartime exigencies.The article concludes by substantiating findings with pertinent diagrams and tables. In essence, the digitization of macrologistical systems entails a holistic amalgamation of digital technologies, data analytics, and decision-making solutions for information management in logistics and supply chain operations. This transformative journey, leveraging technologies such as the Internet of Things, unmanned technologies, identification technologies, blockchain, big data, robotic systems, artificial intelligence, and neural networks, aspires to amplify efficiency, visibility, and coordination throughout the supply chain. This paradigm shift is pivotal for evaluating the efficacy of transport and logistics companies, determining their competitive stance in today’s market. Future research trajectories encompass formulating strategies for national companies operating in the domestic logistics market, identifying and implementing key competitive advantages, and devising measures and tools to ensure efficiency during wartime.

Synergies of research, design-based education and industry collaboration: a dialogue with Marco ten Hoor, Academy Director of the Hotel Management School Leeuwarden

ABSTRACT This edited transcript documents a dialogue between Rodney Westerlaken, Editor-in-Chief of Research in Hospitality Management, serving as host, and Marco Ten Hoor, Academy Director of the Hotel Management School Leeuwarden. The conversation explores multifaceted dimensions of the hospitality sector, encompassing the changing landscape of education, the influence of research, and the urgent research imperatives arising from contemporary global circumstances, such as the COVID-19 pandemic. The conversation offers valuable insights into the future trajectory of hospitality education and research, emphasising the pivotal role of synergistic partnerships between academia and industry. Furthermore, it underscores the significance of harnessing emerging technologies as catalysts for innovation in the industry.

A Motion Logic Network for Pedestrian Motion Prediction

Accurate and fast motion prediction such as pedestrian motion prediction (PMP) is crucial for safe autonomous driving. Much research effort has been devoted to studying the reactive behaviors of pedestrians, such as the interaction between pedestrians or the interaction between pedestrians and the environment. However, compared with behavioral logic, the current motion state of pedestrians has a greater influence on the future trajectory. In this work, we propose a motion logic network (MLN) to improve both the accuracy and efficiency of pedestrian motion prediction. Compared with the traditional data-driven neural networks, the concept of motion logic is directly introduced into the network so that the training of the network is not required. In particular, instead of fitting the network based on inputs and target outputs, the proposed method directly adopts motion logic to predict the future trajectory. To illustrate the performance of the proposed MLN, several experiments have been performed. For acceleration and deceleration motion in practical experiment, the average displacement error (ADE) of MLN has an improvement of as high as 6.25% than CVM’s, while the final displacement error (FDE) of MLN has an improvement of 11.1%. In terms of efficiency, MLN is a hundred times faster than LSTM. It indicates that motion logic plays an important role in the development of prediction algorithms for pedestrian motion. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the effects of pedestrians’ different motion states on the future trajectory. For example, the pedestrians’ state may also change drastically in some situations such as dashing across the road or stopping in anticipation of a bicycle crossing the path. This work explores the use of pedestrians’ physical motion states to develop a network that emphasizes the logical features of pedestrian motion so as to directly estimate the future motion trajectory and trend based on the motion logic. For the first time, the concept of motion logic and the resultant training-free motion logic network (MLN) is proposed, which takes into account the accuracy and efficiency performance of the algorithm. Compared with the state-of-the-art pedestrian prediction methods, the proposed method can better predict the trajectory of pedestrians in more complex motion states. Moreover, a series of simulations and practical experiments with pseudo-constant velocity motion and acceleration/deceleration motion was taken to verify the performance of the proposed MLN.